Uatc, LLC

Neil Traft

Galen Clark Haynes

Charles R Hogg, Iii

Skanda Shirdhar

Anthony Joseph Stentz

Nemanja Djuric

Jeff Schneider

Micol Marchetti-Bowick

Tian Lan

Vladan Radosavljevic

Tzu-Kuo Huang

Alexander David Styler

Tsung-Han Lin

Ian Dewancker

Huahua Wang

Systems and methods for detecting a surprise or unexpected movement of an actor with respect to an autonomous vehicle are provided. An example computer-implemented method can include, for a first compute cycle, obtaining motion forecast data based on first sensor data collected with respect to an actor relative to an autonomous vehicle; and determining, based on the motion forecast data, failsafe region data representing an unexpected path or area where a likelihood of the actor following the unexpected path or entering the unexpected area is below a threshold. For a second compute cycle after the first compute cycle, the method can include obtaining second sensor data; determining, based on the second sensor data and the failsafe region data, that the actor has followed the unexpected path or entered the unexpected area; and in response to such determination, determining a deviation for controlling a movement of the autonomous vehicle.

Systems and methods for detecting surprise movements of an actor with respect to an autonomous vehicle

The present disclosure provides systems and methods for predicting the future locations of objects that are perceived by autonomous vehicles. An autonomous vehicle can include a prediction system that, for each object perceived by the autonomous vehicle, generates one or more potential goals, selects one or more of the potential goals, and develops one or more trajectories by which the object can achieve the one or more selected goals. The prediction systems and methods described herein can include or leverage one or more machine-learned models that assist in predicting the future locations of the objects. As an example, in some implementations, the prediction system can include a machine-learned static object classifier, a machine-learned goal scoring model, a machine-learned trajectory development model, a machine-learned ballistic quality classifier, and/or other machine-learned models. The use of machine-learned models can improve the speed, quality, and/or accuracy of the generated predictions.

Machine learning for predicting locations of objects perceived by autonomous vehicles

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Pittsburgh, PA, United States of America

Neil Traft